Sentienz Cognizer

Relevance and precision are the paramount of information retrieval today and this has lead to exploding research in this area triggering various methodologies
and tools that try to address specific problem domains,

Cognitive models of information retrieval rest on the mix of areas such as cognitive science, human-computer interaction, information retrieval, and library
science. They describe the relationship between a person's cognitive model of the information sought and the organization of this information in an information system. These models attempt to
understand how a person is searching for information so that the database and the search of this database can be designed in such a way as to best serve the user. Cognitive modeling is
typically accomplished with sophisticated deeplearning algorithms.

Information retrieval may incorporate multiple tasks and cognitive problems, particularly because different people may have different methods for attempting
to find this information and expect the information to be in different forms. Cognitive models of information retrieval attempts at something as apparently prosaic as improving search results
or may be something more complex, such as attempting to create a database which can be queried with natural language search.

Current State

Cognizer Overview

Sentienz Cognizer platform was architected ground up keeping in mind the latency, scalability, accuracy and affordability. To this effect we have architected the
product around a highly efficient Graph Store, and have applied cutting edge innovations in distributed computing and cognitive text mining algorithms. Hence, we are able to scale fast, and
perform better with no extra costs of software licenses or excess storage capacities. The cognitive search algorithms set us apart from the rest of the players in the market. Over a period of
time, the machine learns the search patterns and likings of the user; transcending to be a personalized tool rather than a common platform.